Low-resolution coarse-grained (CG) models are widely adopted for studying soft materials. However, there exist many unanswered questions regarding the first step in constructing a CG model - i.e., how best to represent a system in reduced detail. In this study, we developed a statistical physics framework for investigating these questions. We adopt the Gaussian Network Model (GNM) as a simple model for proteins. Given the GNM, we can analytically evaluate all properties of the protein at any arbitrary CG resolution. We assess the quality of each CG representation using a variety of metrics. We implement Monte Carlo (MC) sampling to sample the space of possible CG representations of the system at a fixed resolution. We find that there exists a phase transition below a certain resolution. We also find that the ability of the model to preserve the slow-low frequency modes is anti-correlated with the amount of information preserved by the model.

Investigation of coarse-grained model representations

ACS Spring Meeting 2021

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.